Study On Short-term Precipitation Forecast Technology In Region

Short-term heavy precipitation can cause natural disasters,thus causing great losses to people's lives,social production and economic development.Short-term forecasting of precipitation can predict the intensity of regional precipitation and the natural disasters caused by it in a few hours,which will help people to do the countermeasures in a timely manner and ensure the safety of the people and the loss of property as much as possible.In this paper,the principal component analysis(PCA)method is used to extract the relevant factors influencing the precipitation,and the main factors affecting the precipitation are selected to solve the many factors that affect the precipitation.High computational complexity,longer training time,slowing down convergence rate,and weakening generalization ability.Secondly,the fuzzy clustering algorithm is used to determine the number of RBG hidden layer nodes,and the K-means clustering(K-means)algorithm is used to select the hidden layer parameters of the radial basis neural network precipitation forecasting model.Then,the performance of the k-means clustering algorithm is affected by the selected initial center,and it is easy to fall into the local optimal problem.The PSO intelligent optimization algorithm with global optimization is introduced to the parameters of the radial basis function neural network(PSO-RBF neural network)based on improved particle swarm optimization(PSO)is proposed to optimize the radial basis function neural network.The results show that the improved PSO-RBF neural network precipitation forecasting model has higher accuracy than the traditional radial basis neural network precipitation forecasting model and thestandard particle swarm optimization.Base neural network precipitation forecasting model,with application value.